Futureproofing your Data Warehouse: [Best practices]

Data warehouse

There’s a lot happening in data arena — Agentic AI is on the rise, people are questioning the effectiveness of traditional methods, and traditional data warehouses are struggling to keep up.

Why? Because modern data warehouses aren’t just dealing with more data — they’re dealing with entirely new types of data.

Look at it in this way: While data warehouses remain focused on structured and semi-structured data, the complexity within these data types has increased significantly. We are talking about –

1. Data Volume and Velocity — Data warehouses now must handle massive amounts of transactional and analytical data flowing in at high speeds from various business systems, requiring efficient storage and processing capabilities.
2. Complex Data Relationships — As businesses capture more granular data points the relationships between different data entities become more complex, making schema design and maintenance challenging.
3. Schema Evolution: Business requirements change frequently, requiring data warehouses to adapt their schemas while maintaining historical data integrity and ensuring backward compatibility.

So, you must adapt to the growing complexity of data types and sources to leverage these advancements effectively.

And here’s how you can do that.

Data warehouse best practices for 2025

1. Using AI platforms for maintaining data quality and optimization

Data warehousing now isn’t just about storing information. It’s also how it empowers us to design a system that can handle multiple sources, complex relations and obviously the ever-growing data volumes while staying efficient and scalable.

And hence incorporating smart indexing, partitioning, and query optimization are essential. These techniques ensure your system stays fast, efficient, and scalable as data volumes grow. (And if not for AI, you will find yourself spending weeks and months to get the best outcome).

That’s where AI solutions like Polestar’s Data Nexus come in — acting as a control tower for your data pipelines with Data profiling engine and​ Gen AI based coding assistant. And this helps in modernizing your data estate too.

2. Structuring your data warehouse for maximum performance

A well-structured data warehouse is designed for structured and semi-structured data analytics and business intelligence (BI). However, many organizations blur the lines between AI/ML workloads and traditional data warehousing, leading to inefficiencies.

AI/ML models require raw, unprocessed data with different performance needs, such as complex I/O patterns and high computational power, while data warehouses are optimized for SQL queries, aggregations, and analytical processing. Forcing both into a single system can compromise their effectiveness.

How to Build a Unified Analytics Foundation

To ensure your data warehouse delivers high-performance analytics, organizations should:

• Optimize schemas for analytical queries rather than raw data storage
• Maintain clear business rules and consistent dimensional models
• Support standard BI tools for streamlined reporting and insights

But What About the Convergence of Data Lakes and Data Warehouses?

Data warehouse and Data Lake Co-existence for Businesses

A common question which organizations often ask: If structured and raw data should be managed separately, how do we justify the merging of data lakes and data warehouses?

The answer is simple: Unified data analytics platforms like Microsoft Fabric don’t change the core function of a data warehouse — they enhance it.

Instead of forcing structured and raw data into the same system, Fabric provides an integrated approach that keeps data warehouses optimized for structured and semi-structured data while making it easier to manage all data types without complex configurations.

Plus, having Lakehouse-centric warehouse is the first to support multi-table transactions and open data formats, removing traditional limitations.

With a powerful SQL engine and access to One Lake’s storage virtualization, businesses can focus on insights and reporting instead of spending time managing infrastructure.

3. Secure access control and governance

It goes without saying that your data security needs to evolve with your data (especially when you have a large user base). Because as the number of users grows, granting and maintaining data access for each user becomes a complicated, time-consuming task.

Hence implementing comprehensive access control and security measures is a foundational best practice in enterprise data warehouse environments.

This begins with establishing fine-grained role-based access control (RBAC) at the schema, table, and object levels, particularly crucial for managing access across different data marts and fact tables.

Organizations must implement column-level security for sensitive dimensions (like customer PII or financial metrics) and row-level security for multi-tenant data models or departmental data segregation.

For example:

• Implement dynamic data masking on sensitive columns in fact tables (e.g., masking credit card numbers while preserving the last 4 digits for analysis)
• Set up row-level filters using context variables for departmental data access (WHERE department_id = CURRENT_DEPARTMENT())
• Create materialized views with pre-filtered sensitive data for specific user groups

1Platform by Polestar Solutions— An ecosystem of AI led apps for Enterprise Data Analytics

1Platform is loved by CDAOs mainly because it is modular and you can use it with hyperscalers like Microsoft, AWS, Databricks etc.

1Platform comprises of individual products & accelerators like:

• Master Data Management Tool
• Data Lake Automation Accelerator
• Gen AI based Coding assistant for Data Engineering pipelines and schemas
• Menu Based Analytics/ Action Card
• CXO Dashboards
• Pre-loaded AI/ML models for Causality & Explainability
• Gen AI Insights Bot (integrated with Teams, Slack etc)
• Industry Specific Functional Plays (Wireframes)

Future of Data warehousing

Now what we are particularly excited to see is how these best practices enable the future data warehousing trends. But at the same time the key isn’t to chase every new trend, but to ensure that your data warehouse remains true to its primary purpose: delivering reliable, actionable insights from your structured and semi-structured data.

Also Read

Top 4 Latest Data Management Trends

Because this sets you up for a better data integration which becomes the base for upcoming AI advancements too. Obviously apart from the technological shift you will also have to come out of ‘data warehousing as a cost centre’ notion. Because in in 2025, it’s the engine of innovation, source of competitive advantage and key to navigating complexities if the data world.

So, our suggestion to you is simple. Embrace AI-driven automation, prioritize seamless